Enhancing Distributed EAs using Proactivity [ Extended Abstract ]

In this abstract we describe a proactive strategy followed by a distributed evolutionary algorithm to adapt its migration policy. The proactive decision is made locally within each subpopulation, ant it is based on the entropy of that subpopulation. In that way, each subpopulation can ask for more/less frequent migrations from its neighbors in order to maintain the genetic diversity at a desired level, thus avoiding the subpopulations to get trapped into local minima. We conduct computational experiments on a set of different problems and it is shown that our proactive approach outperforms classical dEA settings by reaching accurate solutions in a lower number of generations.